Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K
Network Function of a Circuit01:25

Network Function of a Circuit

278
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
278
Network Covalent Solids02:18

Network Covalent Solids

13.4K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
13.4K
Social Exchange Theory02:06

Social Exchange Theory

34.5K
We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).
34.5K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

66
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
66
Observational Learning01:12

Observational Learning

158
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
158

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

The bZIP54 (GBF2)-SARD1 module regulates salicylic acid-mediated resistance to Pst DC3000 in Arabidopsis.

Plant physiology·2026
Same author

CRISPR/Cas9-based knockout of BnaLYK compromises pattern-triggered immunity and resistance to Sclerotinia sclerotiorum in Brassica napus.

BMC plant biology·2026
Same author

Dynamic range extension of a Shack-Hartmann wavefront sensor based on an image processing and sorting method.

Applied optics·2026
Same author

Pm6 from Triticum timopheevii encodes an NLR receptor that directly recognizes AvrPm6 to confer powdery mildew resistance in wheat.

Molecular plant·2025
Same author

Tetrastichus brontispae exploits host-derived On-miR-281-3p to suppress the phenoloxidase pathway and facilitate its development.

Pest management science·2025
Same author

Phase response measurement and dynamic distortion correction of a spatial light modulator using the Shack-Hartmann wavefront sensor.

Optics letters·2025

相关实验视频

Updated: Jun 19, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.1K

基于网络表示的社会网络法医分析模型学习学习.

Kuo Zhao1,2,3, Huajian Zhang1, Jiaxin Li1

  • 1School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China.

Entropy (Basel, Switzerland)
|July 26, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用网络代表性学习来识别犯罪网络中的关键人物的社交网络法医分析模型. 该模型增强了对应复杂犯罪活动的关系分析.

关键词:
升降梯度更新 升降梯度更新一个层次化的集群.网络代表性学习学习学习节点矢量化 节点矢量化节点2vec算法 节点2vec算法社交网络 法医医学 社交网络 法医医学

更多相关视频

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

相关实验视频

Last Updated: Jun 19, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.1K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

科学领域:

  • 计算机科学 计算机科学
  • 犯罪学 犯罪学
  • 数据挖掘 数据挖掘

背景情况:

  • 从数字通信中产生大量数据需要先进的法医分析.
  • 识别犯罪网络中的关键人物和领导结构对于执法至关重要.

研究的目的:

  • 引入一个社会网络法医分析 (SNFA) 模型,用于识别和分析犯罪网络中的关键人物.
  • 利用网络表示学习来改善法医调查中的关系分析.

主要方法:

  • 集成传统的网络取证与社区算法和网络表示学习 (Deepwalk,Line,Node2vec).
  • 采用修改的随机步行采样 (BFS,DFS) 和连续的词袋与层次软max用于节点矢量化.
  • 利用等级聚类与共弦值和欧几里德距离测量来确定节点影响和等级.

主要成果:

  • 成功向量化犯罪网络节点,同时保留基本特征和结构信息.
  • 在犯罪网络中的节点内部关系分析中实现了更高的精度.
  • 优化聚类,以准确识别关键人物和领导结构.

结论:

  • 该SNFA模型有效地识别了犯罪网络中的关键人物和领导者.
  • 该模型提高了关系分析的准确性,为打击复杂犯罪活动提供了先进的工具.
  • 网络表示学习为数字法医调查提供了一个强大的框架.